Field of Invention
The present invention relates to a stereoscopic image quality assessment method, and more particularly to an objective assessment method for a stereoscopic image quality combined with manifold characteristics and binocular characteristics.
Description of Related Arts
The quantitative assessment for the stereoscopic image quality is a challenging problem in the image processing field. The stereoscopic image is different from the plane image that the stereoscopic image comprises two different viewpoints. When people view a stereoscopic image, the human visual system (HVS) does not separately process the left viewpoint image and the right viewpoint image, but generates a composite cyclopean image after complex binocular fusion and rivalry. The composite cyclopean image depends on not only the individual stimulating factor difference, but also the geometrical relationship between the two viewpoints. Thus, the quality of the stereoscopic image is related to not only the respective quality of the left viewpoint and the right viewpoint, but also the binocular visual perception.
The perception quality assessment method is to obtain an assessment result having a relatively high consistency with the visual perception quality through simulating the overall perception mechanism of the HVS. The excellent image quality assessment method is required to well reflect the visual perception characteristics of the human eyes. The conventional image quality assessment method based on the structure obtains the image quality from the structure information of the image, such as the edge and the contrast of the image, while the image quality assessment method based on the characteristics of the HVS assesses the image quality mainly from the distortion attention and perception ability of the human eyes. The above methods assess the image quality from the nonlinear geometrical structure of the image and the perception of the human eyes. However, some researches indicate that: for the visual perception phenomenon, the manifold is the basis of the perception and the human brain perceives the objects in the manifold manner; and, the natural scene image generally comprises the manifold structure and has the nonlinear nature of the manifold. Thus, combined with the manifold characteristics, the image quality of the single viewpoint in the stereoscopic image is able to be relatively well assessed.
On the other hand, for the binocular perception, when the left viewpoint and the right viewpoint are symmetrically distorted, it is relatively easy to assess the stereoscopic image quality, and two kinds of conventional methods are described as follows. The first kind of methods is to respectively assess the two viewpoints of the stereoscopic image with the quality assessment method for the plane image, and then average the quality values of the two viewpoints to obtain the quality value of the stereoscopic image. The second kind of methods is to assess the stereoscopic image quality with the left viewpoint and the right viewpoint of the stereoscopic image and the depth/disparity information of the stereoscopic image, wherein the depth/disparity information is assumed to have a certain effect on assessing the stereoscopic image quality by the HVS. However, the second kind of methods has two problems need to be considered. Firstly, the real depth/disparity image is not always useable, so that the second kind of methods generally assesses the quality of the depth perception of the stereoscopic image when estimating the depth image, and thus the accuracy of the depth/disparity estimation algorithm may greatly affect the assessment performance. Secondly, the depth/disparity information may not be related to the three-dimensional perception quality, which has been proved in the article of Kaptein et al. In the subjective experiment, Kaptein adopts the blurred images of the same object under the different depths, and find that, in the three-dimensional display, the depth does not affect the image perception quality to a certain extent.
Although above-mentioned problems exist, when assessing the symmetrically distorted stereoscopic image quality, the two kinds of methods achieve the relatively good quality assessment performance. However, if the left viewpoint and the right viewpoint comprise different degrees or different types of distortions (also called asymmetrical distortion stimulation), the above two kinds of methods both have a relatively poor quality assessment performance. The asymmetrical distortion leads to the more challenging stereoscopic image quality assessment problem, mainly because the quality of the composite cyclopean image generated by the human eyes is related to the distortion type and distribution of the left viewpoint and the right viewpoint. For example, two distorted stereoscopic images are provided, wherein the left viewpoint images of the two distorted stereoscopic images are both the similar high quality images, and the right viewpoint images of the two distorted stereoscopic images are both the similar low quality images. Through counting the observation results of the subjects, it is found that: for the distorted stereoscopic image whose right viewpoint has the white noise, the subjects think the quality of the composite virtual viewpoint image (namely the cyclopean image) is nearer to the low quality right viewpoint images; and, for the distorted stereoscopic image whose right viewpoint has the Gaussian blur, the subjects think the quality of the composite virtual viewpoint image (namely the cyclopean image) is nearer to the high quality left viewpoint images. Thus, effectively simulating the perception mechanism of the binocular asymmetrical distortion of the HVS is one of the keys to increase the performance of the stereoscopic image quality assessment algorithm. The reasonable effective binocular model is able to consider the binocular perception characteristics of the human eyes more comprehensive, and meanwhile increase the assessment effect on the symmetrically distorted and asymmetrically distorted stereoscopic image.